Current demands for high density and performance associated with ultra large scale integration of fabricated devices require submicron features, increased transistor and circuit speeds, and improved reliability. As semiconductor processes progress, pattern dimensions such as line width, and other types of critical dimensions, are continuously shrunken. This is also referred to as the design rule. Such demands require formation of device features with high precision and uniformity, which, in turn, necessitates monitoring of the fabrication process, including frequent and detailed inspections of the devices while they are still in the form of semiconductor wafers, including both finished devices and/or unfinished devices.
The term “specimen” used in this specification should be expansively construed to cover any kind of wafer, masks, and other structures, combinations and/or parts thereof used for manufacturing semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor-fabricated articles.
The complex manufacturing process of specimens is not error-free and such errors may cause faults in manufactured devices. The faults may include defects that can harm operation of the device, and nuisances, which may be defects, but do not cause any harm or malfunction of the manufactured device. By way of non-limiting examples, defects may be caused during the manufacturing process, due to faults in the raw material, mechanical, electrical or optical errors, human errors or others. Further, defects may be caused by spatio-temporal factors, such as temperature changes of the wafer occurring after one or more manufacturing stages during the examination process, which may cause some deformations of the wafer. The examination process can also introduce further alleged errors, for example due to optical, mechanical or electrical problems in the examination equipment or process, which thus provide imperfect captures. Such errors may produce false positive findings, which may seem to contain a defect, but no actual defect exists at the area.
In many applications, the type, or class, of a defect is of importance. For example, defect may be classified into one of a number of classes, such as a particle, a scratch, process, or the like.
Unless specifically stated otherwise, the term “examination” used in this specification should be expansively construed to cover any kind of detection and/or classification of defects in an object. Examination is provided by using, e.g., non-destructive examination tools during or after manufacture of the object to be examined. By way of non-limiting example, the examination process can include scanning (in a single or in multiple scans), sampling, reviewing, measuring, classifying and/or other operations provided with regard to the object or parts thereof, using one or more examination tools. Likewise, examination can be provided prior to manufacture of the object to be examined and can include, for example, generating an examination recipe(s). It is noted that, unless specifically stated otherwise, the term “examination” or its derivatives used in this specification are not limited with respect to the size of the inspected area(s), to the speed or resolution of the scanning or to the type of examination tools. A variety of non-destructive examination tools includes, by way of non-limiting example, optical tools, scanning electron microscopes, atomic force microscopes, etc.
Examination process can include a plurality of examination steps. During the manufacturing process, the examination steps can be performed a multiplicity of times, for example after the manufacturing or processing of certain layers, or the like. Additionally or alternatively, each examination step can be repeated multiple times, for example for different wafer locations or for the same wafer locations with different examination settings.
By way of non-limiting example, run-time examination can employ a two-step procedure, e.g. inspection of a specimen followed by review of sampled defects. During the inspection step, the surface of a specimen or a part thereof (e.g. areas of interest, hot spots, etc.) is typically scanned at relatively high-speed and/or low-resolution. The captured inspection image is analyzed in order to detect defects and obtain locations and other inspection attributes thereof. At the review step the images of at least part of defects detected during the inspection phase are, typically, captured at relatively low speed and/or high-resolution, thereby enabling classification and, optionally, other analyses of at least part of defects. In some cases both phases can be implemented by the same inspection tool, and, in some other cases, these two phases are implemented by different inspection tools.
Most often, the goal of examination is to provide high sensitivity to defects of interest while suppressing detection of nuisance and noise on the wafer. There is a need in the art for improving the sensitivity of defect detection.
General Description
In accordance with certain aspects of the presently disclosed subject matter, there is provided computerized system of capable of classifying defects in a specimen, the system comprising a processing and memory circuitry (PMC) configured to: obtain one or more defect clusters detected on a defect map of the specimen, each given defect cluster characterized by a respective set of cluster attributes comprising one or more spatial attributes, wherein the one or more spatial attributes include spatial density indicative of density of defects in one or more regions on the defect map accommodating the given defect cluster, and each given defect cluster is detected at least based on the spatial density thereof meeting a density criterion; for each given defect cluster, apply a cluster classifier to a respective set of cluster attributes thereof to associate the given defect cluster with one or more labels of a predefined set of labels, wherein the cluster classifier is trained using cluster training data comprising a plurality of pre-labelled defect clusters and cluster attributes thereof; and identify DOI in each given defect cluster by performing a defect filtration for each given defect cluster using one or more filtering parameters, wherein the one or more filtering parameters are specified in accordance with the label of the given defect cluster.
In addition to the above features, the system according to this aspect of the presently disclosed subject matter can comprise one or more of features (i) to (xii) listed below, in any desired combination or permutation which is technically possible:                (i). The defect map can comprise non-clustered defects, and the PMC can be further configured to identify DOI in the non-clustered defects by performing a defect filtration for the non-clustered defects using one or more filtering parameters, and combine the identified DOI in each given defect cluster and the identified DOI in the non-clustered defects to provide an overall DOI information of the specimen.        (ii). The one or more spatial attributes can be selected from a group comprising spatial density, area, defect count, shape, and aspect ratio of the one or more regions on the defect map accommodating the given defect cluster.        (iii). The set of cluster attributes can further comprise a filter rate related attribute.        (iv). The filter rate related attribute can be a cluster filter rate indicative of the number of defects filtered out when applying a defect filter in a given defect cluster as relative to the total number of defects in the given defect cluster. The defect filter can be trained using defect training data comprising a plurality of pre-classified defects and defect attributes thereof.        (v). The criterion can be specified differently for defect clusters with different spatial characterizations.        (vi). Each label can be indicative of a given defect cluster class. The given defect cluster class can at least represent that a defect cluster classified thereto comprises an expected percentage of a specific category of defects or a type of defects in a specific category that meets classification criterion of the given defect cluster class. The specific category can be selected from a group constituted of Defects of Interest (DoI) and nuisance.        (vii). The predefined set of labels can comprise a first label and a second label. The first label can be indicative of a first class at least representing that a defect cluster classified thereto comprises an expected percentage of DOI that meets a first class classification criterion, and the second label can be indicative of a second class at least representing that a defect cluster classified thereto comprises an expected percentage of nuisance that meets a second class classification criterion.        (viii). The one or more filtering parameters can comprise a working point.        (ix). The defect filtration for the non-clustered defects can be performed using a defect filter with a training working point. The defect filter can be trained in a training process based on defect training data comprising a plurality of pre-classified defects and defect attributes thereof. The training working point is a working point selected to be used in the training process.        (x). The working point specified in accordance with the label of a given defect cluster can be selected as relative to the training working point based on a classification sensitivity corresponding to the label.        (xi). The predefined set of labels can comprise a first label and a second label. The first label can be indicative of a first class at least representing that a defect cluster classified thereto comprises an expected percentage of DOI that meets a first class classification criterion, and the second label can be indicative of a second class at least representing that a defect cluster classified thereto comprises an expected percentage of nuisance that meets a second class classification criterion.                    The working point specified in accordance with the first label can be selected as a sensitive working point as relative to the training working point, and the working point specified in accordance with the second label can be selected as an aggressive working point as relative to the training working point.                        (xii). The system can further comprise an examination tool configured to examine the specimen and obtain the defect map thereof.        
In accordance with another aspect of the presently disclosed subject matter, there is provided a computerized method of classifying defects in a specimen, the method comprising: obtaining, by an I/O interface, one or more defect clusters detected on a defect map of the specimen, each given defect cluster characterized by a respective set of cluster attributes comprising one or more spatial attributes, wherein the one or more spatial attributes include spatial density indicative of density of defects in one or more regions on the defect map accommodating the given defect cluster, and each given defect cluster is detected at least based on the spatial density thereof meeting a density criterion; for each given defect cluster, applying, by a processing and memory circuitry (PMC) operatively connected to the I/O interface, a cluster classifier to a respective set of cluster attributes thereof to associate the given defect cluster with one or more labels of a predefined set of labels, wherein the cluster classifier is trained using cluster training data comprising a plurality of pre-labelled defect clusters and cluster attributes thereof; and identifying, by the PMC, DOI in each given defect cluster by performing a defect filtration for each given defect cluster using one or more filtering parameters, wherein the one or more filtering parameters are specified in accordance with the label of the given defect cluster.
This aspect of the disclosed subject matter can comprise one or more of features (i) to (xii) listed above with respect to the system, mutatis mutandis, in any desired combination or permutation which is technically possible.
In accordance with another aspect of the presently disclosed subject matter, there is provided a non-transitory computer readable storage medium tangibly embodying a program of instructions that, when executed by a computer, cause the computer to perform a method of method of classifying defects in a specimen, the method comprising: obtaining one or more defect clusters detected on a defect map of the specimen, each given defect cluster characterized by a respective set of cluster attributes comprising one or more spatial attributes, wherein the one or more spatial attributes include spatial density indicative of density of defects in one or more regions on the defect map accommodating the given defect cluster, and each given defect cluster is detected at least based on the spatial density thereof meeting a density criterion; for each given defect cluster, applying a cluster classifier to a respective set of cluster attributes thereof to associate the given defect cluster with one or more labels of a predefined set of labels, wherein the cluster classifier is trained using cluster training data comprising a plurality of pre-labelled defect clusters and cluster attributes thereof; and identifying DOI in each given defect cluster by performing a defect filtration for each given defect cluster using one or more filtering parameters, wherein the one or more filtering parameters are specified in accordance with the label of the given defect cluster.
This aspect of the disclosed subject matter can comprise one or more of features (i) to (xii) listed above with respect to the system, mutatis mutandis, in any desired combination or permutation which is technically possible.